The field of the invention is systems and methods for magnetic resonance imaging (“MRI”). More particularly, the invention relates to systems and methods for quantitative susceptibility mapping using MRI.
When placed in an external magnetic field, such as the B0 field of an MRI scanner, the magnetic susceptibility of non-ferromagnetic biomaterials generate local magnetic fields in the scanner. This susceptibility is an important physical property of tissue and has emerged as a new contrast mechanism in MRI.
Quantitative susceptibility mapping (“QSM”) is an imaging technique that provides high anatomical contrast and measurements of tissue susceptibility based on biomaterial compositions. As a result, QSM can be advantageously used for monitoring iron overload in diseases such as in Alzheimer's disease; for monitoring demyelinating diseases, such as Parkinson's disease and multiple sclerosis; for monitoring calcifications in the brain and other tissues; and for performing functional MRI. Further applications include monitoring iron overload in the liver and for use in tractography with susceptibility tensor imaging.
However, the potential of QSM MRI is hindered by the inadequacy of the algorithms that are used to process imaging data, requiring long scan times and producing imaging artifacts. A challenge in QSM is that the algorithms used for generating a quantitative susceptibility map from magnetic field shift and susceptibility data are based on a mathematical relationship that is an “ill-posed” inverse problem, which produces unresolvable ambiguities in the absence of additional data. Additional information can be imposed to calculate unambiguous solutions via data from acquisitions at multiple orientations to the magnetic field. While this approach remains the gold standard for accurate susceptibility maps, it is not feasible in a clinical setting. Single-orientation datasets can impose additional constraints in the computation through numerical “regularization.” This approach is, however, limited in its ability to address the challenges posed by the QSM inversion problem.
In clinically obtained datasets, images are routinely plagued by large heterogeneities and susceptibility variations. In addition, images can suffer from limited scan times and patient motion caused by discomfort. As a result, issues in the tissue masking, phase unwrapping, background field removal, and regularized field inversion algorithms used in susceptibility mapping can be significant, generating regions of large signal fluctuations and poor data consistency. As such, the resulting susceptibility maps are highly subject to streaking artifacts, which severely limits their ability for reliable analysis in the clinic. While modifications and manual processing of these datasets can reduce these issues, ideally an automated, fast, and robust technique would be applied for clinical compatibility.
The most widely applied algorithm for QSM is Morphology Enabled Dipole Inversion (“MEDI”), which exploits the structural consistency between the susceptibility map and the magnitude image reconstructed from the same gradient echo MRI. In principle, the contrast change, or “edge” on a magnitude image, arises from the underlying change of tissue type, which is the same cause for the change of magnetic susceptibility. But, images produced by MEDI still exhibit undesirable artifacts in whole-volume processing.
There are several existing methods that can mitigate streaking artifacts in QSM, but they are not without their drawbacks. As one example, thresholding algorithms can be modified to aggressively mask all field estimates at the periphery of the brain. This approach, however, erodes visualization of the brain and, in many cases, also erodes regions of good field estimates. As a consequence of this processing, susceptibility values may be lost in clinically important regions.
As another example of processing techniques for removing streaking artifacts, higher regularization penalties can be imposed in the optimization, though at a severe cost to the resolution of the resulting quantitative susceptibility map.
It would therefore be desirable to provide a method for producing quantitative susceptibility maps that overcomes the drawbacks and limitations of currently existing algorithms. In particular, it would be desirable to provide a method that reduces artifacts in the generated quantitative susceptibility maps, and that is more computationally efficient than currently existing algorithms.